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1 Customer-base concentration, profitability and distress across the corporate life cycle Paul J. Irvine Neeley School of Business Texas Christian University Shawn Saeyeul Park Terry College of Business University of Georgia August, 2014 Çelim Yıldızhan Terry College of Business University of Georgia Corresponding author Paul Irvine, Neeley School of Business The authors wish to thank seminar participants at the University of Georgia, Georgia State University, TCU, the FMA doctoral consortium, AAA annual meeting, Linda Bamber, Lauren Cohen, Lee Cohen, Emmanuel De George, David Folsom, Stu Gillan, Sara Holland, Steven Lim, DJ Nanda, and Lin Zou. We also thank Lauren Cohen, Elizabeth Demers and Philip Joos for generously sharing their data.

2 Customer-base concentration, profitability and distress across the corporate life cycle Abstract Using a recently expanded data set on supplier-customer links, we examine how customer concentration affects firm profitability. We find that the relation between customer concentration and firm profitability is more complex than recent literature suggests. We confirm that customer concentration promotes operating effi ciencies for profitable firms. However, we find a different result for younger, less profitable firms where customer concentration impairs firm profitability and can increase distress risk. We explain these differences by introducing a relationship life-cycle hypothesis wherein the relation between customer-base concentration and profitability is time-varying; being significantly negative in the early years of the relationship, and turning positive as the relationship matures. The key driver of this dynamic is the customer-specific investments the relationship entails. These investments result in larger fixed costs and greater operating leverage early in the relationship, but can significantly benefit the firm as the relationship matures. JEL Classifications: L25; M41; G31; G33 Keywords: Customer concentration, customer-specific investments, life-cycle, selling, general and administrative expense, profitability, default risk.

3 1 Introduction Winning the business of a major customer is an exciting event in the life of the firm. Business from major customers can increase firm revenues markedly and permit effi ciencies of scale in operations and delivery. Despite these advantages, economists have long warned of the danger of supplying a considerable fraction of firm output to a particular customer. Lustgarten (1975) credits Galbraith (1952) with the origin of the concept that large customers are threats to manufacturer s operating profits. The problem with major customers is that margin improvements that the firm can receive, through selling effi ciencies or other economies of scale, do not necessarily accrue to the firm. Major customers recognize their strong bargaining position and can engage in ex-post renegotiation over the contract terms (Klein, Crawford and Alchian (1978), Williamson (1979)). Once the firm has committed resources to production for a major customer, these customer-specific investments represent costs that the firm cannot fully recover unless they can complete the order for the customer. Major customers can impair firm profitability by demanding price concessions, extended trade credit or other benefits. For example, Balakrishnan, Linsmeier and Venkatachalan (1996) argue that major customers are aware of the firm s cost savings from JIT adoption, and that subsequent customer demands for concessions prevent the adopters from improving profitability. 1 In his empirical study of customer concentration, Lustgarten (1975) concludes that high customer concentration (at the industry level) reduces firm profitability. Patatoukas (2012) challenges the conventional wisdom that customer concentration impairs firm profitability. Using SFAS 14 and SEC Reg S-K mandated disaggregated revenue disclosures available from Compustat, he creates a firm-specific measure of customer concentration and finds a positive relation between customer concentration and accounting rates of return. Patatoukas (2012) points out that a narrow focus on customer concentration and gross margins can obscure the effects of customer concentration on key valuation metrics such as accounting rates of return. Highlighting the ability of the DuPont profitability analysis to make this point, Patatoukas (2012) 1 Recently, Ng (2013) relates the example of Procter and Gamble who plan to extend the time they take to pay suppliers from 45 days to 75 days. 2

4 naturally follows earlier studies on firm profitability (Fairfield and Yohn (2001), Soliman (2008)), and focuses on firms with positive operating performance. While this sample selection criterion is often unavoidable in valuation research, such as the case where negative current earnings cannot be capitalized, the criterion can be avoided in a study of supplier-customer relations. Taking advantage of a recent expansion in this data set, we extend the Patatoukas (2012) analysis to include firms with negative operating performance and re-examine the relation between customer concentration and firm profitability over the period. We find that the relation between customer concentration and profitability is more complex than a simple positive or negative relation. While we find that many of Patatoukas (2012) conclusions about profitable firms hold using the expanded data set, we also show that they are not generalizable to firms with negative operating performance. Such firms tend to be younger, their sales more dependent on major customers, they encounter greater demand uncertainty, and they face a higher probability of financial distress. The adverse impact of customer concentration on unprofitable firms produces a negative relation between customer concentration and firm profitability in the full sample. These results challenge us to synthesize our findings with Patatoukas (2012). To do so, we develop a life-cycle hypothesis about the effects of customer concentration on firm profitability. We show that the relationship between customer concentration and firms operating risk and profitability largely reflects the differing costs and benefits that occur throughout the relationship life cycle. Motivated by Williamson (1979) and Anderson, Banker and Janakiraman (2003) we emphasize the importance of customer-specific selling, general and administrative (SG&A) investments in explaining these life-cycle effects. We find that early in the relationship firms with higher customer concentration make more customer-specific SG&A expenses believing that such investments will lead to the operating effi ciencies documented in Patatoukas (2012). However, customer-specific SG&A expenses are, by definition, less transferable than general SG&A investments and thus increase the fixity of SG&A costs, leading to higher operating leverage. To document this relation, we show that the elasticity of SG&A costs with respect to sales is lower in firms with higher customer concentration. These negative effects are ameliorated as the relationship matures, eventually firms 3

5 with high levels of customer concentration are rewarded with higher operating profits, consistent with Patatoukas (2012) results. Our results also suggest that customer concentration is one potential explanation for the Banker, Byzalov and Plehn-Dujowich s (2014) counterintuitive finding that cost elasticity is inversely related to demand uncertainty. After establishing that customer concentration is negatively related to cost elasticity we then show that higher customer concentration is positively related to demand uncertainty. This is logical as firms with major customers have relatively undiversified sources of revenue, and their customer-specific investments prevent them from easily finding alternative sales when faced with declining demand from their major customers. Consistent with this argument, we find that demand uncertainty monotonically increases from the lowest customer concentration quintile to the highest customer concentration quintile. For firms with concentrated customer bases, a higher level of demand uncertainty exacerbates the increase in operating leverage. These operating risks have significant effects on capital structure and firm failure. We find that customer concentration is positively related to the probability of firm failure in our sample. Extending the sample to firms that have no major customers, we find that firms with the lowest customer concentration values have lower probabilities of failure than firms that have no major customers. This result is explained by the fact that the lowest customer concentration firms have less debt compared to firms that have no major customers, even though these two distinct groups of firms have similar levels of profitability, cash holdings and idiosyncratic volatility. More generally, customer concentration profoundly affects capital structure: The higher the customer concentration index, the lower the amount of debt in the firm s capital structure. This result is consistent with the proposition that firms with concentrated customer bases constrain leverage to protect themselves from the devastating effect of losing a major customer (Banerjee, Dasgupta, and Kim (2008)). We find that credit ratings deteriorate as customer concentration increases, thus adding a cost dimension to the Banerjee et al. (2008) hypothesis. Facing increases in operating leverage and demand uncertainty, a concentrated customer base is 4

6 a risky choice for firms. However, as shown by Patatoukas (2012) customer concentration can lead to operating effi ciencies and the possibility of achieving higher profits in the future. By examining a subsample where the age of the supplier-customer link can be identified, we find a number of results that are consistent with our life-cycle hypothesis which we use to explain the dichotomy between profitable and unprofitable firms. We document an important benefit from having a major customer by showing that the initial year of the relationship leads to significant growth in firm sales. We also find that as the relationship matures, the initial adverse effects of customer concentration reverse, leading to improvements in firm operating margins and profitability. These results are consistent with our contention that the initial relationship-specific-costs major customers entail can eventually pay off in significant benefits for the firm. The equity market appears to recognize the net benefits from these relationships as positive abnormal returns are associated with changes in customer concentration. A major contribution of this paper is that it identifies the existence and magnitudes of both the costs and benefits of customer concentration and how they vary over the relationship life cycle. Knowledge of both the costs and benefits of customer concentration is important to managers making the crucial decision of whether to make customer-specific investments in the relationship between the firm and a major customer. Our ability to document the costs and benefits involved in this decision supports the usefulness of mandated disaggregated revenue disclosures and, as in Patatoukas (2012), highlights some of the benefits of improving disaggregated information about firms operations. 2 Hypothesis Development In contrast to the traditional view that major customers can extract benefits from their suppliers and thus lower firm profitability, there are several reasons why major customers could be beneficial to the firm. All orders are different, in either their design, manufacture or logistical delivery. Meeting the demands of many small customers is expensive and firms can achieve economies of scale from dealing with a few major customers. Volume discounts to large customers are common 5

7 and reflect these economies. Although a number of small orders can produce the same total sales as a single large order, the firm faces the problem of customer retention and acquisition. As customer retention and acquisition can be expensive, by dealing with a few major customers, firms can potentially reduce these costs. Cohen and Schmidt (2009) document some of the benefits of attracting large clients and Carlton (1978) outlines how a lower customer-per-firm ratio helps the firm coordinate pricing and production decisions. Jap and Ganesan (2000), Fee, Hadlock and Thomas (2006) and Costello (2013) show how covenant restrictions and customer equity stakes can alleviate contracting problems arising in the relationship. Investigating the empirical evidence on customer concentration and firm profitability, Patatoukas (2012) cites two studies (Newmark (1989) and Kalwani and Narayandas (1995)) that challenge Lustgarten s (1975) finding that customer concentration reduces profitability. Faced with this mixed evidence, Patatoukas (2012) argues that whether major customers are beneficial or detrimental to the firm is ultimately an empirical issue. He answers that question in the affi rmative by showing that customer concentration leads to improved profitability. Firms achieve this profitability through effi ciencies in SG&A expenses, inventory turnover and cash conversion improvements. However, Patatoukas (2012) only examines firms with positive profits. To understand how customer concentration is related to firm profitability across the full range of profitability, we develop several hypotheses focusing on why the relation between customer concentration and firm profitability varies across the relationship life cycle. First, we hypothesize that the nature of the firm s customer base affects the fixity of SG&A expenses (Anderson, Banker and Janakiraman (2003)). Higher customer concentration leads firms to make customer-specific SG&A investments to capture operating effi ciencies that come with major-customer relationships. Such customer-specific investments, by definition, are less transferable to other uses than more general investments. Firms with high customer concentration thus tend to have a larger fixed cost component in their SG&A expenses. If this contention is true, then the elasticity of SG&A expenses with respect to sales should be lower the more concentrated the firm s customer base. 6

8 Second, we hypothesize that a firm with high customer concentration faces higher demand uncertainty. This occurs because firms with only a few major customers have relatively undiversified sources of revenue, and their customer-specific investments prevent them from easily finding alternative sales when faced with declining demand from their major customers. Firms with higher customer concentration are more exposed to idiosyncratic demand shocks generated by their major customers because when major customers receive their own demand shocks, they transfer these demand shocks to their suppliers. Third, we hypothesize that higher customer concentration increases firms operating risk. This hypothesis is a natural extension of our initial two hypotheses, as higher fixed costs lead to increases in operating leverage which, coupled with higher demand uncertainty, increases operating risk. We expect the amplification of operating risk that comes with higher customer concentration to manifest itself through the credit risk channel leading to higher failure probability and higher cost of debt. Fourth, we hypothesize that the relation between customer concentration and firms operating risk and performance largely reflect the different cost and benefits that occur throughout the relationship life-cycle. Since accounting research has not previously addressed how the life-cycle of major customer relationships can affect firms operating risk and performance, to construct our hypotheses we draw on the literature in marketing and management. The literature we cite routinely studies the impact of major suppliers on dependent retailers rather than that of major customers on dependent suppliers. However, from the theoretical and survey evidence provided we can infer general principles that guide our exploration of how the life-cycle of the relationship affects firm profitability. 2 Wilson (1995) discusses how the major customer relationship presents the firm with both costs and benefits. The key features of his model incorporate relationship-specific investments that provide both potential value but also increase operating risk. Wilson (1995) focuses on the lifecycle of the relationship and posits that the success of the relationship can vary dynamically. This 2 The limited accounting research that addresses life-cycle issues (Anthony and Ramesh (1992); Dickinson (2011)) examines the life cycle of the firm rather than the life-cycle of the major customer relationship. 7

9 suggestion is key as it is necessary for our hypothesis to establish that there must be different stages, with different costs and benefits, in the life cycle of the relationship. However, in Wilson s (1995) theory, the value of the relationship to the parties involved depends on hard-to-measure concepts such as trust, cooperation and commitment. 3 Jap and Ganesan (2000) also introduce a dynamic framework when they outline how the optimal contract to deal with relationship-specific investments can change over the relationship life-cycle. Finally, both Jap and Anderson (2007) and Eggert, Ulaga and Schultz (2006) use survey data to formalize the supplier-customer relationship into exploration, build up, maturity and decline stages and provide evidence on how the concepts outlined by Wilson (1995) can change over the life-cycle. Jap and Anderson (2007) find that the decline phase of the relationship can last for a considerable period and suggest that this reflects the fact that relationship-specific investments can have surprisingly long lives. Eggert et al. (2006) conclude that the value created from major customer relationships can increase over time but this potential requires great commitment by both parties during the exploration and build-up phases. From this literature we infer two broad principles of major customer relationships that guide our empirical investigation. First, the relationship is dynamic and that optimal profitability for the firm may not occur until the relationship reaches its maturity phase. The second principle is that the often explosive growth of the relationship during the exploration and build-up phases requires relatively high relationship-specific investments early in the life-cycle. While Jap and Anderson (2007) find that these idiosyncratic investments can often provide surprisingly long-lived benefits, the Eggert et al. (2006) finding that optimal profitability often occurs later in the relationship suggests that these relationship-specific investments can increase costs during the early stages of the relationship life-cycle. These principles suggest that we can expect customer concentration to have a negative impact on firm profitability early in the relationship where profitability is impaired by the build up of customer-specific investments. However, if the relationship succeeds, then suppliers can expect 3 Schloetzer (2012) provides a fine example of an attempt to outline the effects of diffi cult to quantify measures such as information sharing and interdependence. 8

10 operating profitability to increase as the relationship matures. We can test these predictions for a subsample where customers can be identified and thus, the age of the link between the supplier and customer firms can be determined (LIN KAGE). Where the necessary data to construct LINKAGE is not available, we use the age of the supplier firm as an instrument for the age of the relationship. Empirically, firm age is highly correlated with the age of major customer relationships, yet still is an inferior instrument relative to LIN KAGE. We also investigate whether firm age reflects the same life-cycle information as LINKAGE. 4 3 Data FASB accounting standards require all public companies to disclose the identities of their major customers representing more than 10% of their total sales. We extract the identities of each firm s major customers from the Compustat Customer Segment Files. We focus on the period between 1977 and Compustat Customer Segment Files provide for each firm the names of its major customers, revenue derived from sales to each major customer, and the type of each major customer. 5 For each firm we determine whether its customers are listed in the CRSP-Compustat database. If they are, then we assign them to the corresponding firm s PERMNO. Since the focus in this paper is on customer concentration and its impact on firms operating and financial performance, even when the customer firm cannot be assigned a PERMNO, we still keep the supplier-customer link in the sample and identify the customer firm as a non CRSP-Compustat company. 6 Following Patatoukas (2012), we construct our primary measure of customer concentration using 4 We recognize the validity of the Eggert et al. (2006) critique that link age itself is an imperfect measure of life-cycle stage as some relationships may be designed to be shorter than others. However, the literature does not supply an alternative instrument. 5 The dataset groups customers into three broad categories based on their type: company (COMPANY), domestic government (GOVDOM), and foreign government (GOVFRN). We exclude information on customers that are identified as domestic or foreign governments, even if they may be major customers for a certain supplier firm. 6 Cohen and Frazzini (2008) report that the Compustat Customer Segment files report the names of customer companies but often fail to provide company identification codes such as customer firms PERMNO s. For these firms, we use a phonetic string matching algorithm to generate a list of potential matches to the customer name. We then hand-match the customer to the corresponding PERMNO based on the firm s name, segment, and SIC code. 9

11 the following formula: CC i,t = n ( ) Sales to 2 Customeri,j,t (1) j=1 T otal Sales i,t If firm i has n major customers in year t, the measure of customer concentration ( CC i,t ) of the firm is defined as the sum of the squares of the sales shares to each major customer. The sales share to each customer j in year t is calculated as the ratio of firm i s sales to customer j in year t scaled by firm i s total sales in year t. Patatoukas (2012) constructs his customer concentration measure in the spirit of the Herfindahl-Hirschman index, and suggests that the measure captures two elements of customer concentration: the number of major customers and the relative importance of each major customer. By definition, the customer concentration (CC) is bounded between 0 and 1 as CC is equal to 1 if the firm earns all of its revenue from a single customer and as the customer base diversifies CC tends to 0. As in Patatoukas (2012), we exclude financial services firms from the sample. Our sample consists of all firms listed in the CRSP-Compustat database with non-negative book values of equity, non-missing values of customer concentration (CC), market value of equity (M V ), annual percentage sales growth (GROW T H), and accounting rates of return at the fiscal year-end when we can identify major customers. 7 After imposing these restrictions, we are left with 49,760 supplier firm-year observations between 1977 and Sample composition Our sample differs from the sample used in Patatoukas (2012). Patatoukas (2012) focuses on the subsample of firm-year observations with positive operating margins, whereas we include firm-year observations with operating losses. Of the 49,760 firm-year observations in our sample, 22,480 firmyear observations have the corresponding CRSP-Compustat customer data necessary to construct LIN KAGE (45.2 percent), while 10,836 firm-year observations have operating losses (21.8 percent). 7 Including firms with both negative earnings and negative book values confounds a direct interpretation of higher ROE as a good outcome. We drop negative book value firms to avoid this confusion. In unreported analysis, we include negative book value firms and find consistent results. 10

12 Using the LINKAGE customer data subset, we can test our relationship life-cycle hypotheses. The latter subset on operating losses allows us to determine if the impact of customer concentration on firm profitability is different for unprofitable firms. Furthermore, over a comparable period we have significantly more firm-year observations with positive operating margins (38,924) than Patatoukas (2012) 25, Descriptive statistics Figure 1 presents the time series of average customer concentration from 1977 to 2007 as reported in the Compustat customer segment files. During this period each supplier averages 1.89 major customers who generate 33 percent of its annual sales. However, each supplier firm accounts for only 2% of their customers cost of goods sold. Over the sample period, customer concentration exhibits a marked increase from the early years of the sample through 1997, a period coincident with a general increase in the number of listed firms. The number of firms reporting customer concentration then falls from a high of close to 3,500 in 1997 to what appears to be a steady state of just over 2,000 for the period. Consistent with Patatoukas (2012), median customer concentration reveals a generally increasing trend over time, from a low of 0.03 in 1977 and 1978 to a high of over 0.06 in Table 1 lists our variable definitions, grouped into two categories: (i) Supplier-firm characteristics, and (ii) Default prediction variables used in our extension of the Campbell, Hilscher and Szilagyi (2008) default prediction model. CC is the basic measure of customer concentration described in Equation (1) and CC measures the year over year change in CC. Table 2 presents summary statistics for several key variables for the full sample (Panel A), the subset of firms with identifiable customers (Panel B), and for positive and negative profitability groups (Panel C). The variables MV, AGE, and GROW T H define the basic characteristics of 8 Hoechle, Schmid, Walter and Yermack (2012) report a temporary deletion of valid Compustat segment file observations during This problem, as well as periodic updates to the Compustat segment files, can account for the difference in sample sizes between our paper and Patatoukas (2012). 9 To alleviate concerns regarding our sample, we repeat all analyses using only the subset of firm-year observations with positive operating margins and find results qualitatively similar to Patatoukas (2012). 11

13 supplier firms. MV measures the firm s market value of equity in millions of dollars. AGE is the firm s age in years, measured from the time of its initial public offering. GROW T H is the supplier firm s annual sales growth rate. ROA, ROE, and SGA, define key operating characteristics of supplier firms. ROA is the ratio of income before extraordinary items to the beginning of year book value of total assets for the firm. ROE is the ratio of income before extraordinary items to the beginning of year book value of equity for the firm. SGA is the ratio of selling, general, and administrative expenses to sales. IHLD is the ratio of inventory to the book value of total assets for the firm. T LMT A and CASHMT A are defined as in Campbell et al. (2008) as total liabilities and total cash scaled by the market value of total assets. Panel A of Table 2 reports the mean, standard deviation, skewness, median, 25th, and 75th percentile values for the key variables in this study. CC averages 10.1% for the 49,760 observations in the sample with a standard of deviation of 14.7%. The latter statistic suggests that there is large cross-sectional variation in firms dependence on their major customers for revenues. Our sample is considerably larger than the restricted sample in Patatoukas (2012), but mean CC is close to the mean in Patatoukas (2012). This fact shows that any differing results due to our expansion of the sample is not attributable to radical differences in customer concentration. Our sample firms are younger and smaller than those in Patatoukas (2012). Firms in our sample average only 10.3 years of age compared to 14.8 in Patatoukas (2012) with a market cap of $806 million relative to Patatoukas (2012) $1,206 million. Because we do not censor on profitability, the average ROA and ROE are lower at (Patatoukas (2012), 0.06) and (0.13), respectively. Three of our main dependent variables, ROA, ROE and SGA, and the key explanatory variable, CC, are all significantly skewed. In order to mitigate the effect of skewness, we use the decile rank of CC ( CC) instead of CC ( CC), as in Patatoukas (2012), in our regression analyses. Panel B of Table 2 examines statistics for the subsample of firms whose customers can be identified and thus, LIN KAGE can be determined. The average and standard deviation of CC are comparable to the full sample at 11.6% and 14.8%. Indeed, the summary statistics of all the key variables are comparable to the full sample. The LIN KAGE subset firms are moderately 12

14 larger with a mean market value of $997.0 million (compared to $806 in the full sample), and have a slightly lower sales growth rate of 20% (22%). 10 Panel C of Table 2 separates the sample into positive and negative operating margin groups. For each group, we report the mean, median, and standard deviation of key variables and report the differences in means across the two groups. Positive operating margin firms dominate the composition of the sample by a ratio of almost 4:1. The differences between these two groups are striking and almost always statistically and economically significant. Negative operating margin (OM) firms have a mean customer concentration of 14.2%, compared to 9.0% for positive OM firms (t-statistic of the difference = -27.6). They are also younger, averaging only 7.3 years compared to 11.1 years for the positive OM subsample (t-statistic of the difference = 48.6). Total liabilities to market assets averages 0.30 for the negative OM firms and 0.36 for positive OM firms. Negative OM firms have more cash to total assets (CASHMT A) at 0.17 relative to the 0.09 cash holdings of positive OM firms. We note by inspection that positive OM firms have more debt and less cash, but both types of firms have significant debt in their capital structure. Firms that are not profitable are, on average, younger, smaller in size, and more reliant on their major customers for their revenues. Furthermore, firms with negative operating margins have significantly higher SG&A expenses as a percentage of their sales than profitable firms. In the rest of the paper we try to understand the differences between firms with positive operating margins and firms with negative operating margins and determine whether our life-cycle hypothesis is a key driver of these differences. 10 Patatoukas (2012, p. 373) also provides evidence that this subset is consistent with the full sample. 13

15 4 Results 4.1 Customer concentration and firm performance Correlation Analysis Table 3 presents Pearson and Spearman correlations across the full sample (Panel A), the positive operating margin subsample (Panel B) and the negative operating margin subsample (Panel C). By analyzing these correlations, we can get an initial idea of how the relation between customer concentration and firm profitability depends on the sign of operating profitability. In the full sample, customer concentration is negatively related to ROA and ROE with correlation coeffi cients of and -0.08, respectively. In the positive OM subsample, the correlations are positive for ROA at 0.03 and ROE at In the negative OM subsample, the signs of these correlations reverse. ROE. 11 Here, the correlation between customer concentration and ROA is and for The correlation between customer concentration and SGA, a key measure of operating effi ciency in Patatoukas (2012), is positive in the full sample, indicating that customer concentration is not generally associated with cost savings. Nevertheless, in the positive OM subsample, the correlations are negative (-0.04), consistent with the findings in Patatoukas (2012). In the negative OM subsample, the sign of the correlation is reversed and relatively large at Customer concentration is negatively correlated with firm age in all three panels, suggesting that younger firms tend to have higher customer concentration. Our tests confirm Patatoukas (2012) finding that customer concentration can be positively related to profitability and that operating effi ciencies associated with customer concentration are a plausible cause for the increased profitability in already profitable firms. We suggest that the contrary results for unprofitable firms largely reflect the differing stages of the relationship life cycle. We hypothesize that early in the life cycle significant relationship-specific expenses can increase costs and impair profitability. The significant positive correlation between customer concentration and SG&A expenses for negative OM firms is consistent with this hypothesis, but these connections 11 Note that the skewed distribution of CC can cause the subsample correlations to fail to bracket the full sample correlation, an illustration of Simpson s paradox. 14

16 should be confirmed controlling for the covariates included in Patatoukas (2012) Regression Analyses We verify the net effect of customer concentration on profitability and costs in Table 4 which presents the average coeffi cients of Fama-MacBeth regressions using six firm operating characteristics as the dependent variables. Following Patatoukas (2012) the independent variables we use are customer concentration rank (Rank(CC)) and control variables for market value (M V ), firm age (AGE), sales growth (GROW T H), an indicator variable for firms having more than one line of business (CONGLO), and financial leverage (F LEV ). The full sample results in Panel A show that inclusion of negative operating margin firms has a profound effect on the relation between customer concentration and firm operations. Unlike Patatoukas (2012, 373) results, customer concentration is negatively related to both ROA and ROE in the full sample. Customer concentration is also negatively related to asset turnover (AT O) and positively related to SG&A expenses. These results show that Patatoukas (2012) results do not generalize to firms with operating losses and that a further explanation is required to explain how customer concentration affects firm profitability. Panel B of Table 4 presents the same analysis for profitable firms only. For these firms and using the same set of control variables, we generally can confirm many of the findings in Patatoukas (2012). Customer concentration is positively related to ROA and ROE as well as profit margin (P M), but we do not confirm, in our larger sample of positive OM firms, that customer concentration has beneficial effects on asset turnover. In line with Patatoukas (2012) and arguments on the impact of customer power in Kelly and Gosman (2000), we find that suppliers with more concentrated customer bases report significantly lower gross margins. Patatoukas (2012) argues that the negative effects on gross margins can be offset if high CC firms spend less on SG&A expenses. As in Patatoukas (2012) we find this offsetting effect exists in this subsample. Positive operating margin firms with higher customer concentration tend to spend significantly less on SG&A expenses. When we examine firms with negative operating margins in Panel C of Table 4, we find that the relation between customer concentration and firm operating characteristics is markedly different 15

17 than it is for firms with positive operating margins. In Panel C, we find that customer concentration has a negative effect on ROE, ROA, and profit margin (P M). Unlike the results for positive operating margin firms in Panel B, the negative impact of customer concentration on gross margins is not offset by lower SG&A expenses. In the SG&A regression reported in Column (6), the coeffi cient on customer concentration is significantly positive. To summarize, we expand upon one of the main tables in Patatoukas (2012, Table 2, Panel A) in Table 4. While we find generally consistent results regarding the effects of customer concentration in the subsample of positive operating margin firms, we find contrary results in the subsample of firms with negative operating margins. Furthermore, the coeffi cients on the rank of customer concentration in the negative operating margin subsample are larger in magnitude and of the opposite sign to those in the subsample of positive operating margin firms Impact of customer concentration on operating leverage and demand uncertainty In Section 2 we develop the hypothesis that expenses and profits vary over the life-cycle of the major customer relationship and this variation can explain the differences we observe in how customer concentration affects profitable and unprofitable firms. The dynamics underlying the life cycle rely on contentions about how the customer base affects firm costs, initially on the patterns of costrigidity in SG&A expenses. To demonstrate the relative importance of SG&A costs in our sample, we first show in Panel A of Table 5 average operating expenses. Cost of goods sold average 64.4% of sales and SG&A expenses average 39.1%. As a component of SG&A expenses, advertising expense averages only 1.0% of sales. 12 Panel B of Table 5 examines the elasticity with respect to sales for cost of goods sold and SG&A expenses, across five different quintiles of customer concentration. Our examination of cost elasticity is derived from the cost-fixity arguments of Anderson et al. (2003) and Baumgarten, 12 The latter figure indicates why the improvements in advertising expenses customer concentration allows do not necessarily translate into operating profitability. 16

18 Bonenkamp and Homburg (2010). Cost elasticity with respect to sales measures the percentage variation in costs relative to percentage variation in firm sales. We find that for all firms, costs are inelastic, varying less than one-to-one with sales variation. We also find a distinct pattern in cost elasticity: the higher a firm s customer concentration, the lower its cost elasticity. The differences are significant across the concentration quintiles, and particularly dramatic for SG&A elasticity. SG&A cost elasticity is 0.79 for firms in the lowest customer concentration quintile falling to 0.56 in the highest customer concentration quintile. Economically, we infer from this data that firms with higher customer concentration make greater fixed investments in customer-specific SG&A expenses. They do this to capture potential operating effi ciencies. Such investments allow firms to more easily expand their operations when major customers increase their demand (Banker et al. 2014). However, when demand falls, these customer-specific fixed investments are less transferable to other customers than more general costs. To understand the effects of major customer demand we examine how sales volatility varies with customer concentration. Banker et al. (2014) postulate that demand uncertainty (measured by the volatility of sales), can lead to lower cost elasticity. They argue that firms facing high demand uncertainty make large fixed investments to capitalize in high-demand states. Firms that do not make such investments would, due to high short-term adjustment costs, be unable to capitalize on the high profits available in high-demand states. Their arguments would dovetail into our findings on cost elasticity and customer concentration if demand uncertainty increases with customer concentration. When we examine demand uncertainty across customer concentration quintiles in Panel C of Table 5, we find that demand uncertainty significantly increases from the lowest customer concentration quintile (0.19) to the highest customer concentration quintile (0.32). If one considers firm sales in a portfolio context, then this finding makes sense. Firms with a few major customers are relatively undiversified in sales and thus, customer-specific demand shocks are more likely to impact their sales compared to firms with diversified customer bases. 17

19 We verify the validity of the univariate sorts conducted in Panels B and C using Fama-MacBeth style regressions in Panel D. The monotonically increasing relation we find between customer concentration and demand uncertainty complements the arguments of both Patatoukas (2012) and Banker et al. (2014). If the relationship encourages firms to make customer-specific investments, then firms will have more inelastic cost structures and potentially higher profits should the relationship succeed. However, the higher fixed costs incurred coupled with higher demand uncertainty could lead to a greater probability of financial distress for these firms. We investigate this issue in detail in the empirical tests below. 4.2 Impact of customer concentration on firm failure and cost of debt Observing that firms with high customer concentration have lower cost elasticity and higher demand uncertainty, we next investigate the relation between customer concentration (CC) and probability of firm failure. For this purpose we replicate the firm failure model of Campbell et al. (2008) to highlight the incremental power of customer concentration to explain financial distress Failure Prediction Earlier we speculate that customer concentration could be risky for supplier firms. We support this contention by analyzing whether our measure of customer concentration Rank(CC) is related to the probability of firm failure. To accomplish this we run a dynamic model predicting firm failure for all firms over the period between 1980 and The dependent variable is the dichotomous outcome variable: firm failure or no failure in a particular firm-year. We use the framework in Campbell, Hilscher, and Szilagyi (2008) who use financial and market variables to predict default. 13 We adopt their nomenclature for the set of predictive variables: Total liabilities to the market value of assets (T LMT A), net income to market value of assets (NIMT A), the standard deviation of stock returns over the previous three months (SIGM A), market to book 13 In unreported results we conduct a similar, albeit a static, failure prediction analysis for firms that recently have gone public in order to assess the impact of customer concentration on the likelihood of firm failure in the five years that immediately follow an IPO. Following Demers and Joos (2007), we obtain qualitatively similar results. 18

20 ratio (MB), relative size of the firm as measured by the log of the market value of the firm relative to the log of market value of the S&P 500 Index (RSIZE), the ratio of firm cash holdings to the market value of total assets (CASHMT A), and the prior month s stock returns relative to the S&P 500 Index returns over the same time period (EXRET ). 14 Campbell et al. (2008) find that this set of independent variables is able to predict default. We examine this finding for our sample in Column (1) of Panel A in Table 6. In this specification, we use the independent variables proposed by Campbell et al. (2008) to estimate the failure probability for 48,948 firm-year observations that have a corresponding customer base concentration value. For our sample of firms with customer concentration data, we find results that confirm the Campbell et al. (2008) model of failure predictability. The model has a psuedo-r 2 of 20.9% and all of the independent variables are significant with the expected sign. In Column (2) of Panel A in Table 6 we add the measure of customer concentration, Rank(CC), to the regression. We find significant results from including the customer-base concentration variable. The coeffi cient on Rank(CC) in Column (2) is positive and significant. This result demonstrates that customer concentration captures failure-related information that is not already reflected in the existing predictors of firm failure. In Column (3) of Panel A we expand the sample to include firms that do not have any major customer data (N occ). Although customer concentration increases the likelihood of failure in sample, we do not know what the global effect of major customers might be. By definition, it is impossible to calculate CC for firms without major customers, so we split up the entire sample into five groups, the N occ group, and four categories of customer concentration. Firms with Rank(CC) values between zero and zero point three are categorized as Rank(CC)_1, firms with Rank(CC) values between zero point three and zero point five are categorized as Rank(CC)_2, firms with Rank(CC) values between zero point five and zero point eight are categorized as Rank(CC)_3 and finally firms with Rank(CC) values between zero point eight and one are grouped under 14 All financial variables are observable 12 months prior to the failure event to avoid endogenous relations being recorded between the predictive variables and the failure event. 19

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